Overview

Dataset statistics

Number of variables34
Number of observations4046
Missing cells30
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory280.0 B

Variable types

Numeric13
Categorical21

Warnings

tempo_min is highly correlated with tempo_maxHigh correlation
tempo_max is highly correlated with tempo_minHigh correlation
acousticness has unique values Unique
energy has unique values Unique

Reproduction

Analysis started2021-04-24 07:04:16.260000
Analysis finished2021-04-24 07:04:56.662207
Duration40.4 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

popularity
Real number (ℝ)

Distinct82
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.003163269673
Minimum-2.547764796
Maximum2.534483659
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:56.823948image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-2.547764796
5-th percentile-1.80402112
Q1-0.6264269655
median0.05533807115
Q30.6751244681
95-th percentile1.542825424
Maximum2.534483659
Range5.082248455
Interquartile range (IQR)1.301551434

Descriptive statistics

Standard deviation1.001928567
Coefficient of variation (CV)-316.7382711
Kurtosis-0.1621771973
Mean-0.003163269673
Median Absolute Deviation (MAD)0.6817650366
Skewness-0.1931886929
Sum-12.7985891
Variance1.003860854
MonotocityNot monotonic
2021-04-24T16:04:57.018818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05533807115126
 
3.1%
0.1173167108125
 
3.1%
-0.06861920823120
 
3.0%
-0.006640568539116
 
2.9%
-0.1305978479115
 
2.8%
0.7990817475104
 
2.6%
0.6131458284104
 
2.6%
0.7371031078104
 
2.6%
0.489188549102
 
2.5%
0.3032526299102
 
2.5%
Other values (72)2928
72.4%
ValueCountFrequency (%)
-2.54776479619
0.5%
-2.48578615611
 
0.3%
-2.42380751712
0.3%
-2.36182887717
0.4%
-2.29985023716
0.4%
-2.23787159712
0.3%
-2.17589295811
 
0.3%
-2.11391431818
0.4%
-2.05193567815
0.4%
-1.98995703928
0.7%
ValueCountFrequency (%)
2.5344836591
 
< 0.1%
2.4725050196
 
0.1%
2.41052637913
0.3%
2.3485477416
0.4%
2.286569118
0.4%
2.2245904610
0.2%
2.16261182117
0.4%
2.10063318113
0.3%
2.0386545413
 
0.1%
1.9146972624
 
0.1%

duration_ms
Real number (ℝ)

Distinct3970
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001787237812
Minimum-2.884596613
Maximum23.1477118
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:57.211861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-2.884596613
5-th percentile-1.295318241
Q1-0.4590092098
median-0.07482097302
Q30.3716680589
95-th percentile1.409657121
Maximum23.1477118
Range26.03230842
Interquartile range (IQR)0.8306772688

Descriptive statistics

Standard deviation1.041431802
Coefficient of variation (CV)582.70466
Kurtosis98.07894066
Mean0.001787237812
Median Absolute Deviation (MAD)0.4172393159
Skewness5.618124572
Sum7.231164188
Variance1.084580198
MonotocityNot monotonic
2021-04-24T16:04:57.404802image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.47237362133
 
0.1%
0.074733937543
 
0.1%
0.049126685073
 
0.1%
-0.15493683762
 
< 0.1%
-0.039551413592
 
< 0.1%
-0.66467394972
 
< 0.1%
0.45234007152
 
< 0.1%
-0.45900920982
 
< 0.1%
0.25334910722
 
< 0.1%
-0.40625460282
 
< 0.1%
Other values (3960)4023
99.4%
ValueCountFrequency (%)
-2.8845966131
< 0.1%
-2.884584391
< 0.1%
-2.8845477211
< 0.1%
-2.8099505551
< 0.1%
-2.8001843531
< 0.1%
-2.8001476841
< 0.1%
-2.7921782671
< 0.1%
-2.7666565751
< 0.1%
-2.7184733821
< 0.1%
-2.7048324781
< 0.1%
ValueCountFrequency (%)
23.14771181
< 0.1%
18.678323411
< 0.1%
14.313576391
< 0.1%
8.2134766011
< 0.1%
7.5736008651
< 0.1%
7.3943378751
< 0.1%
5.5971976731
< 0.1%
5.5622031321
< 0.1%
5.44686661
< 0.1%
5.1556039651
< 0.1%

acousticness
Real number (ℝ)

UNIQUE

Distinct4046
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0235402161
Minimum-1.427928103
Maximum2.761557589
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:57.608418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1.427928103
5-th percentile-1.055626739
Q1-0.8007430872
median-0.3775769511
Q30.763542496
95-th percentile1.997811001
Maximum2.761557589
Range4.189485692
Interquartile range (IQR)1.564285583

Descriptive statistics

Standard deviation1.009683531
Coefficient of variation (CV)42.89185479
Kurtosis-0.601362352
Mean0.0235402161
Median Absolute Deviation (MAD)0.5526320908
Skewness0.8073989991
Sum95.24371433
Variance1.019460832
MonotocityNot monotonic
2021-04-24T16:04:57.801358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2351250681
 
< 0.1%
1.0021623351
 
< 0.1%
-0.53020971681
 
< 0.1%
-0.2697606711
 
< 0.1%
-1.0330122031
 
< 0.1%
-0.42550289811
 
< 0.1%
-0.7467822191
 
< 0.1%
1.1620280831
 
< 0.1%
-1.0502634591
 
< 0.1%
-0.52137295551
 
< 0.1%
Other values (4036)4036
99.8%
ValueCountFrequency (%)
-1.4279281031
< 0.1%
-1.3882348641
< 0.1%
-1.3761046451
< 0.1%
-1.3636369131
< 0.1%
-1.3311999771
< 0.1%
-1.3203461711
< 0.1%
-1.2867819671
< 0.1%
-1.2849463521
< 0.1%
-1.2625500471
< 0.1%
-1.2591037161
< 0.1%
ValueCountFrequency (%)
2.7615575891
< 0.1%
2.6729845361
< 0.1%
2.5933036921
< 0.1%
2.5862075841
< 0.1%
2.5760953691
< 0.1%
2.5319209431
< 0.1%
2.5247910961
< 0.1%
2.5208534741
< 0.1%
2.5188772961
< 0.1%
2.5106656181
< 0.1%

positiveness
Real number (ℝ)

Distinct4036
Distinct (%)100.0%
Missing10
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-0.008422509483
Minimum-2.091252726
Maximum2.350240973
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:57.999331image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-2.091252726
5-th percentile-1.508881085
Q1-0.8508728357
median-0.07075482423
Q30.8024790466
95-th percentile1.676869979
Maximum2.350240973
Range4.441493699
Interquartile range (IQR)1.653351882

Descriptive statistics

Standard deviation1.010011185
Coefficient of variation (CV)-119.9180823
Kurtosis-0.9867580037
Mean-0.008422509483
Median Absolute Deviation (MAD)0.8222946089
Skewness0.1623388092
Sum-33.99324827
Variance1.020122595
MonotocityNot monotonic
2021-04-24T16:04:58.194363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.0419276971
 
< 0.1%
-0.67796693781
 
< 0.1%
-0.89436868841
 
< 0.1%
-0.50892503841
 
< 0.1%
-0.3241158091
 
< 0.1%
-0.41727571961
 
< 0.1%
-0.56187806181
 
< 0.1%
-0.54210904381
 
< 0.1%
0.71067788411
 
< 0.1%
-0.74352095511
 
< 0.1%
Other values (4026)4026
99.5%
(Missing)10
 
0.2%
ValueCountFrequency (%)
-2.0912527261
< 0.1%
-2.0869182751
< 0.1%
-2.0578691191
< 0.1%
-2.0535578711
< 0.1%
-2.0482735521
< 0.1%
-2.0406332881
< 0.1%
-2.0105126261
< 0.1%
-2.002417231
< 0.1%
-1.9819840921
< 0.1%
-1.9786699821
< 0.1%
ValueCountFrequency (%)
2.3502409731
< 0.1%
2.304764631
< 0.1%
2.2542893971
< 0.1%
2.2469345171
< 0.1%
2.224036821
< 0.1%
2.1936832511
< 0.1%
2.1894022191
< 0.1%
2.1766906981
< 0.1%
2.1669867041
< 0.1%
2.1566712111
< 0.1%

danceability
Real number (ℝ)

Distinct4038
Distinct (%)100.0%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.01626275488
Minimum-3.050910669
Maximum3.115611032
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:58.392458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-3.050910669
5-th percentile-1.66855317
Q1-0.6826163533
median0.05782158769
Q30.723007927
95-th percentile1.622891722
Maximum3.115611032
Range6.1665217
Interquartile range (IQR)1.40562428

Descriptive statistics

Standard deviation0.9905767277
Coefficient of variation (CV)60.91075806
Kurtosis-0.3650289427
Mean0.01626275488
Median Absolute Deviation (MAD)0.6990722528
Skewness-0.1122536721
Sum65.66900419
Variance0.9812422534
MonotocityNot monotonic
2021-04-24T16:04:58.587064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.056901317011
 
< 0.1%
0.067685958671
 
< 0.1%
0.91470294931
 
< 0.1%
0.17979968321
 
< 0.1%
-1.1752790081
 
< 0.1%
0.40966512191
 
< 0.1%
-0.084964396621
 
< 0.1%
0.32082528961
 
< 0.1%
-1.5188912031
 
< 0.1%
-0.45844167971
 
< 0.1%
Other values (4028)4028
99.6%
(Missing)8
 
0.2%
ValueCountFrequency (%)
-3.0509106691
< 0.1%
-2.8763475621
< 0.1%
-2.7199050891
< 0.1%
-2.719210871
< 0.1%
-2.718242931
< 0.1%
-2.7016935871
< 0.1%
-2.679838441
< 0.1%
-2.6707499651
< 0.1%
-2.6553340251
< 0.1%
-2.6347680461
< 0.1%
ValueCountFrequency (%)
3.1156110321
< 0.1%
3.0308943371
< 0.1%
2.7143499161
< 0.1%
2.644778341
< 0.1%
2.5576812031
< 0.1%
2.4953694211
< 0.1%
2.4650835571
< 0.1%
2.4639909161
< 0.1%
2.4473367221
< 0.1%
2.435328581
< 0.1%

loudness
Real number (ℝ)

Distinct4025
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.009769729991
Minimum-7.443616879
Maximum1.895475826
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:58.791811image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-7.443616879
5-th percentile-1.862838098
Q1-0.5183771796
median0.1201658956
Q30.6912972889
95-th percentile1.341130546
Maximum1.895475826
Range9.339092705
Interquartile range (IQR)1.209674468

Descriptive statistics

Standard deviation1.014802943
Coefficient of variation (CV)-103.8721586
Kurtosis3.574320252
Mean-0.009769729991
Median Absolute Deviation (MAD)0.6019524476
Skewness-1.25118394
Sum-39.52832754
Variance1.029825013
MonotocityNot monotonic
2021-04-24T16:04:58.987566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.89547582622
 
0.5%
0.87452824221
 
< 0.1%
0.2338275951
 
< 0.1%
-0.50683454921
 
< 0.1%
0.80907162541
 
< 0.1%
0.6828844621
 
< 0.1%
0.48837584321
 
< 0.1%
-0.51910093671
 
< 0.1%
1.2453116461
 
< 0.1%
-0.85707760731
 
< 0.1%
Other values (4015)4015
99.2%
ValueCountFrequency (%)
-7.4436168791
< 0.1%
-6.5587993561
< 0.1%
-5.9615631951
< 0.1%
-5.9445759711
< 0.1%
-5.904052751
< 0.1%
-5.8841308211
< 0.1%
-5.6116747951
< 0.1%
-5.3940581061
< 0.1%
-5.1017344441
< 0.1%
-4.5748254441
< 0.1%
ValueCountFrequency (%)
1.89547582622
0.5%
1.8944238111
 
< 0.1%
1.8837687631
 
< 0.1%
1.8808516661
 
< 0.1%
1.8803439371
 
< 0.1%
1.8789538081
 
< 0.1%
1.8738124421
 
< 0.1%
1.8589760221
 
< 0.1%
1.8570981881
 
< 0.1%
1.850043371
 
< 0.1%

energy
Real number (ℝ)

UNIQUE

Distinct4046
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01267567298
Minimum-3.001608584
Maximum1.960778862
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:59.179482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-3.001608584
5-th percentile-1.875917215
Q1-0.7173662782
median0.1387697522
Q30.8094211977
95-th percentile1.338651639
Maximum1.960778862
Range4.962387445
Interquartile range (IQR)1.526787476

Descriptive statistics

Standard deviation1.00092756
Coefficient of variation (CV)-78.96445113
Kurtosis-0.4607817362
Mean-0.01267567298
Median Absolute Deviation (MAD)0.7301868994
Skewness-0.5279728089
Sum-51.28577287
Variance1.001855979
MonotocityNot monotonic
2021-04-24T16:04:59.412548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8760931981
 
< 0.1%
1.2270242291
 
< 0.1%
-1.0243014851
 
< 0.1%
-0.28863940551
 
< 0.1%
-2.0414904431
 
< 0.1%
1.2336738041
 
< 0.1%
0.5732215861
 
< 0.1%
-0.48886068861
 
< 0.1%
1.0995008611
 
< 0.1%
-1.1832417151
 
< 0.1%
Other values (4036)4036
99.8%
ValueCountFrequency (%)
-3.0016085841
< 0.1%
-2.9857894491
< 0.1%
-2.9637055851
< 0.1%
-2.9317392211
< 0.1%
-2.8473953781
< 0.1%
-2.8256417921
< 0.1%
-2.8210218681
< 0.1%
-2.7957824611
< 0.1%
-2.7801604171
< 0.1%
-2.7502663611
< 0.1%
ValueCountFrequency (%)
1.9607788621
< 0.1%
1.8500608081
< 0.1%
1.8362321791
< 0.1%
1.8188160121
< 0.1%
1.8034293291
< 0.1%
1.7991023731
< 0.1%
1.7939944191
< 0.1%
1.7625163771
< 0.1%
1.7533658931
< 0.1%
1.7395160891
< 0.1%

liveness
Real number (ℝ)

Distinct4043
Distinct (%)100.0%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.009395747361
Minimum-1.710188376
Maximum4.684112073
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:04:59.616921image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1.710188376
5-th percentile-0.994839096
Q1-0.6325733206
median-0.313125069
Q30.3217448567
95-th percentile2.105094591
Maximum4.684112073
Range6.394300449
Interquartile range (IQR)0.9543181773

Descriptive statistics

Standard deviation0.9960368316
Coefficient of variation (CV)-106.0093246
Kurtosis4.616321055
Mean-0.009395747361
Median Absolute Deviation (MAD)0.4032007967
Skewness1.97877549
Sum-37.98700658
Variance0.9920893699
MonotocityNot monotonic
2021-04-24T16:04:59.829385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.54124918241
 
< 0.1%
-0.98310712411
 
< 0.1%
-0.66496572951
 
< 0.1%
-0.71773184641
 
< 0.1%
1.2963621311
 
< 0.1%
-0.81757068791
 
< 0.1%
-0.36040012321
 
< 0.1%
-0.18972107931
 
< 0.1%
-0.63966535591
 
< 0.1%
-0.69227379991
 
< 0.1%
Other values (4033)4033
99.7%
(Missing)3
 
0.1%
ValueCountFrequency (%)
-1.7101883761
< 0.1%
-1.5270330791
< 0.1%
-1.4805573171
< 0.1%
-1.4743168361
< 0.1%
-1.4514166961
< 0.1%
-1.4348853421
< 0.1%
-1.4099823141
< 0.1%
-1.3989767121
< 0.1%
-1.38540031
< 0.1%
-1.3769456211
< 0.1%
ValueCountFrequency (%)
4.6841120731
< 0.1%
4.654719081
< 0.1%
4.5718058541
< 0.1%
4.51468881
< 0.1%
4.4427373721
< 0.1%
4.3619766361
< 0.1%
4.339230781
< 0.1%
4.335970651
< 0.1%
4.3272486431
< 0.1%
4.3047016971
< 0.1%

speechiness
Real number (ℝ)

Distinct4038
Distinct (%)100.0%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-0.01833804451
Minimum-2.353504484
Maximum8.070790445
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:05:00.108377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-2.353504484
5-th percentile-1.163088302
Q1-0.6055712017
median-0.2001258912
Q30.2913265831
95-th percentile1.995029889
Maximum8.070790445
Range10.42429493
Interquartile range (IQR)0.8968977848

Descriptive statistics

Standard deviation0.9821987437
Coefficient of variation (CV)-53.56071326
Kurtosis7.038617042
Mean-0.01833804451
Median Absolute Deviation (MAD)0.4448174072
Skewness1.982871931
Sum-74.04902373
Variance0.9647143721
MonotocityNot monotonic
2021-04-24T16:05:00.319770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3881857251
 
< 0.1%
-0.50527125671
 
< 0.1%
-0.88861169831
 
< 0.1%
0.064640634461
 
< 0.1%
-1.1033273291
 
< 0.1%
-0.77993657721
 
< 0.1%
-0.81184962061
 
< 0.1%
1.1820778661
 
< 0.1%
-0.7804833651
 
< 0.1%
-0.71089879261
 
< 0.1%
Other values (4028)4028
99.6%
(Missing)8
 
0.2%
ValueCountFrequency (%)
-2.3535044841
< 0.1%
-2.0872626791
< 0.1%
-2.0563366621
< 0.1%
-2.0198670911
< 0.1%
-1.9519301281
< 0.1%
-1.9307158571
< 0.1%
-1.8987019871
< 0.1%
-1.8850713971
< 0.1%
-1.866177231
< 0.1%
-1.8521473181
< 0.1%
ValueCountFrequency (%)
8.0707904451
< 0.1%
7.8665533671
< 0.1%
6.4741084441
< 0.1%
6.4211701891
< 0.1%
5.9127370291
< 0.1%
5.8946820081
< 0.1%
5.5676830961
< 0.1%
5.422790591
< 0.1%
5.2209028391
< 0.1%
5.0608293121
< 0.1%

instrumentalness
Real number (ℝ)

Distinct4045
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.006088727172
Minimum-1.386365599
Maximum5.053412247
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:05:00.511896image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1.386365599
5-th percentile-0.7029797573
Q1-0.463575377
median-0.2806028481
Q3-0.06333766952
95-th percentile2.763165785
Maximum5.053412247
Range6.439777847
Interquartile range (IQR)0.4002377075

Descriptive statistics

Standard deviation0.9935366624
Coefficient of variation (CV)-163.1764134
Kurtosis7.915340044
Mean-0.006088727172
Median Absolute Deviation (MAD)0.1959540922
Skewness2.88130283
Sum-24.62890141
Variance0.9871150994
MonotocityNot monotonic
2021-04-24T16:05:00.704134image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.21939459571
 
< 0.1%
-0.4941274631
 
< 0.1%
-0.70526565481
 
< 0.1%
-0.34909153451
 
< 0.1%
-0.060064411171
 
< 0.1%
-0.27317485221
 
< 0.1%
1.9938410011
 
< 0.1%
-0.52949607211
 
< 0.1%
3.6472143111
 
< 0.1%
-0.14343269881
 
< 0.1%
Other values (4035)4035
99.7%
ValueCountFrequency (%)
-1.3863655991
< 0.1%
-1.216054061
< 0.1%
-1.1739173831
< 0.1%
-1.1487237371
< 0.1%
-1.1107261061
< 0.1%
-1.0710075381
< 0.1%
-1.049433641
< 0.1%
-1.0288242861
< 0.1%
-1.0246241921
< 0.1%
-1.0151688031
< 0.1%
ValueCountFrequency (%)
5.0534122471
< 0.1%
4.6576684971
< 0.1%
4.6238670271
< 0.1%
4.5245268421
< 0.1%
4.50485761
< 0.1%
4.4830091521
< 0.1%
4.4524189131
< 0.1%
4.4180919481
< 0.1%
4.3886459131
< 0.1%
4.3812200191
< 0.1%

region_region_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
-0.04721629222182335
4033 
21.17913018883343
 
13

Length

Max length20
Median length20
Mean length19.99036085
Min length17

Characters and Unicode

Total characters80881
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04721629222182335
2nd row-0.04721629222182335
3rd row-0.04721629222182335
4th row-0.04721629222182335
5th row-0.04721629222182335
ValueCountFrequency (%)
-0.047216292221823354033
99.7%
21.1791301888334313
 
0.3%
2021-04-24T16:05:01.069892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:01.183796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.047216292221823354033
99.7%
21.1791301888334313
 
0.3%

Most occurring characters

ValueCountFrequency (%)
224211
29.9%
18118
 
10.0%
38118
 
10.0%
08079
 
10.0%
84072
 
5.0%
.4046
 
5.0%
44046
 
5.0%
74046
 
5.0%
94046
 
5.0%
-4033
 
5.0%
Other values (2)8066
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72802
90.0%
Other Punctuation4046
 
5.0%
Dash Punctuation4033
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
224211
33.3%
18118
 
11.2%
38118
 
11.2%
08079
 
11.1%
84072
 
5.6%
44046
 
5.6%
74046
 
5.6%
94046
 
5.6%
64033
 
5.5%
54033
 
5.5%
ValueCountFrequency (%)
-4033
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80881
100.0%

Most frequent character per script

ValueCountFrequency (%)
224211
29.9%
18118
 
10.0%
38118
 
10.0%
08079
 
10.0%
84072
 
5.0%
.4046
 
5.0%
44046
 
5.0%
74046
 
5.0%
94046
 
5.0%
-4033
 
5.0%
Other values (2)8066
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII80881
100.0%

Most frequent character per block

ValueCountFrequency (%)
224211
29.9%
18118
 
10.0%
38118
 
10.0%
08079
 
10.0%
84072
 
5.0%
.4046
 
5.0%
44046
 
5.0%
74046
 
5.0%
94046
 
5.0%
-4033
 
5.0%
Other values (2)8066
 
10.0%

region_region_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.6 KiB
-0.30118604566219687
3721 
3.32020694319144
 
325

Length

Max length20
Median length20
Mean length19.67869501
Min length16

Characters and Unicode

Total characters79620
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.30118604566219687
2nd row-0.30118604566219687
3rd row-0.30118604566219687
4th row-0.30118604566219687
5th row-0.30118604566219687
ValueCountFrequency (%)
-0.301186045662196873721
92.0%
3.32020694319144325
 
8.0%
2021-04-24T16:05:01.529717image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:01.652611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.301186045662196873721
92.0%
3.32020694319144325
 
8.0%

Most occurring characters

ValueCountFrequency (%)
615209
19.1%
011813
14.8%
111813
14.8%
87442
9.3%
34696
 
5.9%
44696
 
5.9%
24371
 
5.5%
94371
 
5.5%
.4046
 
5.1%
-3721
 
4.7%
Other values (2)7442
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71853
90.2%
Other Punctuation4046
 
5.1%
Dash Punctuation3721
 
4.7%

Most frequent character per category

ValueCountFrequency (%)
615209
21.2%
011813
16.4%
111813
16.4%
87442
10.4%
34696
 
6.5%
44696
 
6.5%
24371
 
6.1%
94371
 
6.1%
53721
 
5.2%
73721
 
5.2%
ValueCountFrequency (%)
-3721
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common79620
100.0%

Most frequent character per script

ValueCountFrequency (%)
615209
19.1%
011813
14.8%
111813
14.8%
87442
9.3%
34696
 
5.9%
44696
 
5.9%
24371
 
5.5%
94371
 
5.5%
.4046
 
5.1%
-3721
 
4.7%
Other values (2)7442
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII79620
100.0%

Most frequent character per block

ValueCountFrequency (%)
615209
19.1%
011813
14.8%
111813
14.8%
87442
9.3%
34696
 
5.9%
44696
 
5.9%
24371
 
5.5%
94371
 
5.5%
.4046
 
5.1%
-3721
 
4.7%
Other values (2)7442
9.3%

region_region_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size331.7 KiB
-0.1469291778721575
3962 
6.806000104826665
 
84

Length

Max length19
Median length19
Mean length18.95847751
Min length17

Characters and Unicode

Total characters76706
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1469291778721575
2nd row-0.1469291778721575
3rd row-0.1469291778721575
4th row6.806000104826665
5th row-0.1469291778721575
ValueCountFrequency (%)
-0.14692917787215753962
97.9%
6.80600010482666584
 
2.1%
2021-04-24T16:05:01.933591image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:02.044579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.14692917787215753962
97.9%
6.80600010482666584
 
2.1%

Most occurring characters

ValueCountFrequency (%)
715848
20.7%
111970
15.6%
28008
10.4%
58008
10.4%
97924
10.3%
04382
 
5.7%
64382
 
5.7%
84130
 
5.4%
.4046
 
5.3%
44046
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68698
89.6%
Other Punctuation4046
 
5.3%
Dash Punctuation3962
 
5.2%

Most frequent character per category

ValueCountFrequency (%)
715848
23.1%
111970
17.4%
28008
11.7%
58008
11.7%
97924
11.5%
04382
 
6.4%
64382
 
6.4%
84130
 
6.0%
44046
 
5.9%
ValueCountFrequency (%)
-3962
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76706
100.0%

Most frequent character per script

ValueCountFrequency (%)
715848
20.7%
111970
15.6%
28008
10.4%
58008
10.4%
97924
10.3%
04382
 
5.7%
64382
 
5.7%
84130
 
5.4%
.4046
 
5.3%
44046
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII76706
100.0%

Most frequent character per block

ValueCountFrequency (%)
715848
20.7%
111970
15.6%
28008
10.4%
58008
10.4%
97924
10.3%
04382
 
5.7%
64382
 
5.7%
84130
 
5.4%
.4046
 
5.3%
44046
 
5.3%

region_region_D
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size331.5 KiB
-0.2307258408598937
3847 
4.334148252632184
 
199

Length

Max length19
Median length19
Mean length18.90163124
Min length17

Characters and Unicode

Total characters76476
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.2307258408598937
2nd row-0.2307258408598937
3rd row-0.2307258408598937
4th row-0.2307258408598937
5th row-0.2307258408598937
ValueCountFrequency (%)
-0.23072584085989373847
95.1%
4.334148252632184199
 
4.9%
2021-04-24T16:05:02.319548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:02.432832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.23072584085989373847
95.1%
4.334148252632184199
 
4.9%

Most occurring characters

ValueCountFrequency (%)
811939
15.6%
011541
15.1%
28291
10.8%
38291
10.8%
57893
10.3%
77694
10.1%
97694
10.1%
44643
 
6.1%
.4046
 
5.3%
-3847
 
5.0%
Other values (2)597
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68583
89.7%
Other Punctuation4046
 
5.3%
Dash Punctuation3847
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
811939
17.4%
011541
16.8%
28291
12.1%
38291
12.1%
57893
11.5%
77694
11.2%
97694
11.2%
44643
 
6.8%
1398
 
0.6%
6199
 
0.3%
ValueCountFrequency (%)
-3847
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76476
100.0%

Most frequent character per script

ValueCountFrequency (%)
811939
15.6%
011541
15.1%
28291
10.8%
38291
10.8%
57893
10.3%
77694
10.1%
97694
10.1%
44643
 
6.1%
.4046
 
5.3%
-3847
 
5.0%
Other values (2)597
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII76476
100.0%

Most frequent character per block

ValueCountFrequency (%)
811939
15.6%
011541
15.1%
28291
10.8%
38291
10.8%
57893
10.3%
77694
10.1%
97694
10.1%
44643
 
6.1%
.4046
 
5.3%
-3847
 
5.0%
Other values (2)597
 
0.8%

region_region_E
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.8 KiB
-0.4480754711406975
3336 
2.23176688840884
710 

Length

Max length19
Median length19
Mean length18.47355413
Min length16

Characters and Unicode

Total characters74744
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4480754711406975
2nd row-0.4480754711406975
3rd row2.23176688840884
4th row-0.4480754711406975
5th row-0.4480754711406975
ValueCountFrequency (%)
-0.44807547114069753336
82.5%
2.23176688840884710
 
17.5%
2021-04-24T16:05:02.678340image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:02.784777image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.44807547114069753336
82.5%
2.23176688840884710
 
17.5%

Most occurring characters

ValueCountFrequency (%)
414764
19.8%
010718
14.3%
710718
14.3%
17382
9.9%
86886
9.2%
56672
8.9%
64756
 
6.4%
.4046
 
5.4%
-3336
 
4.5%
93336
 
4.5%
Other values (2)2130
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number67362
90.1%
Other Punctuation4046
 
5.4%
Dash Punctuation3336
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
414764
21.9%
010718
15.9%
710718
15.9%
17382
11.0%
86886
10.2%
56672
9.9%
64756
 
7.1%
93336
 
5.0%
21420
 
2.1%
3710
 
1.1%
ValueCountFrequency (%)
-3336
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common74744
100.0%

Most frequent character per script

ValueCountFrequency (%)
414764
19.8%
010718
14.3%
710718
14.3%
17382
9.9%
86886
9.2%
56672
8.9%
64756
 
6.4%
.4046
 
5.4%
-3336
 
4.5%
93336
 
4.5%
Other values (2)2130
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII74744
100.0%

Most frequent character per block

ValueCountFrequency (%)
414764
19.8%
010718
14.3%
710718
14.3%
17382
9.9%
86886
9.2%
56672
8.9%
64756
 
6.4%
.4046
 
5.4%
-3336
 
4.5%
93336
 
4.5%
Other values (2)2130
 
2.8%

region_region_F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.5 KiB
-0.19451237690311818
3910 
5.141061026147839
 
136

Length

Max length20
Median length20
Mean length19.89915966
Min length17

Characters and Unicode

Total characters80512
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.19451237690311818
2nd row-0.19451237690311818
3rd row-0.19451237690311818
4th row-0.19451237690311818
5th row-0.19451237690311818
ValueCountFrequency (%)
-0.194512376903118183910
96.6%
5.141061026147839136
 
3.4%
2021-04-24T16:05:03.054726image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:03.947723image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.194512376903118183910
96.6%
5.141061026147839136
 
3.4%

Most occurring characters

ValueCountFrequency (%)
120094
25.0%
08092
10.1%
97956
 
9.9%
37956
 
9.9%
87956
 
9.9%
44182
 
5.2%
64182
 
5.2%
.4046
 
5.0%
54046
 
5.0%
24046
 
5.0%
Other values (2)7956
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72556
90.1%
Other Punctuation4046
 
5.0%
Dash Punctuation3910
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
120094
27.7%
08092
11.2%
97956
 
11.0%
37956
 
11.0%
87956
 
11.0%
44182
 
5.8%
64182
 
5.8%
54046
 
5.6%
24046
 
5.6%
74046
 
5.6%
ValueCountFrequency (%)
-3910
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80512
100.0%

Most frequent character per script

ValueCountFrequency (%)
120094
25.0%
08092
10.1%
97956
 
9.9%
37956
 
9.9%
87956
 
9.9%
44182
 
5.2%
64182
 
5.2%
.4046
 
5.0%
54046
 
5.0%
24046
 
5.0%
Other values (2)7956
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII80512
100.0%

Most frequent character per block

ValueCountFrequency (%)
120094
25.0%
08092
10.1%
97956
 
9.9%
37956
 
9.9%
87956
 
9.9%
44182
 
5.2%
64182
 
5.2%
.4046
 
5.0%
54046
 
5.0%
24046
 
5.0%
Other values (2)7956
 
9.9%

region_region_G
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size331.7 KiB
-0.1168503948705801
3986 
8.55795139680588
 
60

Length

Max length19
Median length19
Mean length18.95551162
Min length16

Characters and Unicode

Total characters76694
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1168503948705801
2nd row-0.1168503948705801
3rd row-0.1168503948705801
4th row-0.1168503948705801
5th row-0.1168503948705801
ValueCountFrequency (%)
-0.11685039487058013986
98.5%
8.5579513968058860
 
1.5%
2021-04-24T16:05:04.222312image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:04.325171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.11685039487058013986
98.5%
8.5579513968058860
 
1.5%

Most occurring characters

ValueCountFrequency (%)
016004
20.9%
812198
15.9%
112018
15.7%
58212
10.7%
94106
 
5.4%
.4046
 
5.3%
64046
 
5.3%
34046
 
5.3%
74046
 
5.3%
-3986
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68662
89.5%
Other Punctuation4046
 
5.3%
Dash Punctuation3986
 
5.2%

Most frequent character per category

ValueCountFrequency (%)
016004
23.3%
812198
17.8%
112018
17.5%
58212
12.0%
94106
 
6.0%
64046
 
5.9%
34046
 
5.9%
74046
 
5.9%
43986
 
5.8%
ValueCountFrequency (%)
-3986
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76694
100.0%

Most frequent character per script

ValueCountFrequency (%)
016004
20.9%
812198
15.9%
112018
15.7%
58212
10.7%
94106
 
5.4%
.4046
 
5.3%
64046
 
5.3%
34046
 
5.3%
74046
 
5.3%
-3986
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII76694
100.0%

Most frequent character per block

ValueCountFrequency (%)
016004
20.9%
812198
15.9%
112018
15.7%
58212
10.7%
94106
 
5.4%
.4046
 
5.3%
64046
 
5.3%
34046
 
5.3%
74046
 
5.3%
-3986
 
5.2%

region_region_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.3 KiB
-0.22350522754362567
3865 
4.474168282282398
 
181

Length

Max length20
Median length20
Mean length19.86579338
Min length17

Characters and Unicode

Total characters80377
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.474168282282398
2nd row-0.22350522754362567
3rd row-0.22350522754362567
4th row-0.22350522754362567
5th row-0.22350522754362567
ValueCountFrequency (%)
-0.223505227543625673865
95.5%
4.474168282282398181
 
4.5%
2021-04-24T16:05:04.617459image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:04.722569image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.223505227543625673865
95.5%
4.474168282282398181
 
4.5%

Most occurring characters

ValueCountFrequency (%)
220049
24.9%
515460
19.2%
77911
 
9.8%
67911
 
9.8%
37911
 
9.8%
07730
 
9.6%
44408
 
5.5%
.4046
 
5.0%
-3865
 
4.8%
8724
 
0.9%
Other values (2)362
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72466
90.2%
Other Punctuation4046
 
5.0%
Dash Punctuation3865
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
220049
27.7%
515460
21.3%
77911
 
10.9%
67911
 
10.9%
37911
 
10.9%
07730
 
10.7%
44408
 
6.1%
8724
 
1.0%
1181
 
0.2%
9181
 
0.2%
ValueCountFrequency (%)
.4046
100.0%
ValueCountFrequency (%)
-3865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80377
100.0%

Most frequent character per script

ValueCountFrequency (%)
220049
24.9%
515460
19.2%
77911
 
9.8%
67911
 
9.8%
37911
 
9.8%
07730
 
9.6%
44408
 
5.5%
.4046
 
5.0%
-3865
 
4.8%
8724
 
0.9%
Other values (2)362
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII80377
100.0%

Most frequent character per block

ValueCountFrequency (%)
220049
24.9%
515460
19.2%
77911
 
9.8%
67911
 
9.8%
37911
 
9.8%
07730
 
9.6%
44408
 
5.5%
.4046
 
5.0%
-3865
 
4.8%
8724
 
0.9%
Other values (2)362
 
0.5%

region_region_I
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size333.6 KiB
-0.49121754559229086
3294 
2.035757901917447
752 

Length

Max length20
Median length20
Mean length19.44241226
Min length17

Characters and Unicode

Total characters78664
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.49121754559229086
2nd row2.035757901917447
3rd row-0.49121754559229086
4th row-0.49121754559229086
5th row-0.49121754559229086
ValueCountFrequency (%)
-0.491217545592290863294
81.4%
2.035757901917447752
 
18.6%
2021-04-24T16:05:04.967118image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:05.077599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.491217545592290863294
81.4%
2.035757901917447752
 
18.6%

Most occurring characters

ValueCountFrequency (%)
911386
14.5%
511386
14.5%
210634
13.5%
08092
10.3%
48092
10.3%
18092
10.3%
76302
8.0%
.4046
 
5.1%
-3294
 
4.2%
83294
 
4.2%
Other values (2)4046
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71324
90.7%
Other Punctuation4046
 
5.1%
Dash Punctuation3294
 
4.2%

Most frequent character per category

ValueCountFrequency (%)
911386
16.0%
511386
16.0%
210634
14.9%
08092
11.3%
48092
11.3%
18092
11.3%
76302
8.8%
83294
 
4.6%
63294
 
4.6%
3752
 
1.1%
ValueCountFrequency (%)
-3294
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common78664
100.0%

Most frequent character per script

ValueCountFrequency (%)
911386
14.5%
511386
14.5%
210634
13.5%
08092
10.3%
48092
10.3%
18092
10.3%
76302
8.0%
.4046
 
5.1%
-3294
 
4.2%
83294
 
4.2%
Other values (2)4046
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII78664
100.0%

Most frequent character per block

ValueCountFrequency (%)
911386
14.5%
511386
14.5%
210634
13.5%
08092
10.3%
48092
10.3%
18092
10.3%
76302
8.0%
.4046
 
5.1%
-3294
 
4.2%
83294
 
4.2%
Other values (2)4046
 
5.1%

region_region_J
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
-0.08496656930187041
4021 
11.769334789159084
 
25

Length

Max length20
Median length20
Mean length19.98764212
Min length18

Characters and Unicode

Total characters80870
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.08496656930187041
2nd row-0.08496656930187041
3rd row-0.08496656930187041
4th row-0.08496656930187041
5th row-0.08496656930187041
ValueCountFrequency (%)
-0.084966569301870414021
99.4%
11.76933478915908425
 
0.6%
2021-04-24T16:05:05.399096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:05.524640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.084966569301870414021
99.4%
11.76933478915908425
 
0.6%

Most occurring characters

ValueCountFrequency (%)
016109
19.9%
612088
14.9%
98117
10.0%
18117
10.0%
88092
10.0%
48092
10.0%
34071
 
5.0%
74071
 
5.0%
.4046
 
5.0%
54046
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72803
90.0%
Other Punctuation4046
 
5.0%
Dash Punctuation4021
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
016109
22.1%
612088
16.6%
98117
11.1%
18117
11.1%
88092
11.1%
48092
11.1%
34071
 
5.6%
74071
 
5.6%
54046
 
5.6%
ValueCountFrequency (%)
-4021
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80870
100.0%

Most frequent character per script

ValueCountFrequency (%)
016109
19.9%
612088
14.9%
98117
10.0%
18117
10.0%
88092
10.0%
48092
10.0%
34071
 
5.0%
74071
 
5.0%
.4046
 
5.0%
54046
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII80870
100.0%

Most frequent character per block

ValueCountFrequency (%)
016109
19.9%
612088
14.9%
98117
10.0%
18117
10.0%
88092
10.0%
48092
10.0%
34071
 
5.0%
74071
 
5.0%
.4046
 
5.0%
54046
 
5.0%

region_region_K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.2 KiB
-0.24607002807249065
3816 
4.063883796954769
 
230

Length

Max length20
Median length20
Mean length19.8294612
Min length17

Characters and Unicode

Total characters80230
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.24607002807249065
2nd row-0.24607002807249065
3rd row-0.24607002807249065
4th row-0.24607002807249065
5th row-0.24607002807249065
ValueCountFrequency (%)
-0.246070028072490653816
94.3%
4.063883796954769230
 
5.7%
2021-04-24T16:05:05.827420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:05.943127image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.246070028072490653816
94.3%
4.063883796954769230
 
5.7%

Most occurring characters

ValueCountFrequency (%)
023126
28.8%
211448
14.3%
68322
 
10.4%
48092
 
10.1%
78092
 
10.1%
94506
 
5.6%
84276
 
5.3%
.4046
 
5.0%
54046
 
5.0%
-3816
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72368
90.2%
Other Punctuation4046
 
5.0%
Dash Punctuation3816
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
023126
32.0%
211448
15.8%
68322
 
11.5%
48092
 
11.2%
78092
 
11.2%
94506
 
6.2%
84276
 
5.9%
54046
 
5.6%
3460
 
0.6%
ValueCountFrequency (%)
-3816
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80230
100.0%

Most frequent character per script

ValueCountFrequency (%)
023126
28.8%
211448
14.3%
68322
 
10.4%
48092
 
10.1%
78092
 
10.1%
94506
 
5.6%
84276
 
5.3%
.4046
 
5.0%
54046
 
5.0%
-3816
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII80230
100.0%

Most frequent character per block

ValueCountFrequency (%)
023126
28.8%
211448
14.3%
68322
 
10.4%
48092
 
10.1%
78092
 
10.1%
94506
 
5.6%
84276
 
5.3%
.4046
 
5.0%
54046
 
5.0%
-3816
 
4.8%

region_region_L
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.5 KiB
-0.16911677335610464
3933 
5.913074026633224
 
113

Length

Max length20
Median length20
Mean length19.91621354
Min length17

Characters and Unicode

Total characters80581
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.16911677335610464
2nd row-0.16911677335610464
3rd row-0.16911677335610464
4th row-0.16911677335610464
5th row-0.16911677335610464
ValueCountFrequency (%)
-0.169116773356104643933
97.2%
5.913074026633224113
 
2.8%
2021-04-24T16:05:06.220240image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:06.354420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.169116773356104643933
97.2%
5.913074026633224113
 
2.8%

Most occurring characters

ValueCountFrequency (%)
615958
19.8%
115845
19.7%
38205
10.2%
08092
10.0%
48092
10.0%
77979
9.9%
.4046
 
5.0%
94046
 
5.0%
54046
 
5.0%
-3933
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72602
90.1%
Other Punctuation4046
 
5.0%
Dash Punctuation3933
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
615958
22.0%
115845
21.8%
38205
11.3%
08092
11.1%
48092
11.1%
77979
11.0%
94046
 
5.6%
54046
 
5.6%
2339
 
0.5%
ValueCountFrequency (%)
-3933
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80581
100.0%

Most frequent character per script

ValueCountFrequency (%)
615958
19.8%
115845
19.7%
38205
10.2%
08092
10.0%
48092
10.0%
77979
9.9%
.4046
 
5.0%
94046
 
5.0%
54046
 
5.0%
-3933
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII80581
100.0%

Most frequent character per block

ValueCountFrequency (%)
615958
19.8%
115845
19.7%
38205
10.2%
08092
10.0%
48092
10.0%
77979
9.9%
.4046
 
5.0%
94046
 
5.0%
54046
 
5.0%
-3933
 
4.9%

region_region_M
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
-0.01925808989634436
4043 
51.92622972384317
 
3

Length

Max length20
Median length20
Mean length19.99777558
Min length17

Characters and Unicode

Total characters80911
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.01925808989634436
2nd row-0.01925808989634436
3rd row-0.01925808989634436
4th row-0.01925808989634436
5th row-0.01925808989634436
ValueCountFrequency (%)
-0.019258089896344364043
99.9%
51.926229723843173
 
0.1%
2021-04-24T16:05:06.668171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:06.782439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.019258089896344364043
99.9%
51.926229723843173
 
0.1%

Most occurring characters

ValueCountFrequency (%)
912135
15.0%
812132
15.0%
012129
15.0%
38092
10.0%
68089
10.0%
48089
10.0%
24055
 
5.0%
14049
 
5.0%
.4046
 
5.0%
54046
 
5.0%
Other values (2)4049
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72822
90.0%
Other Punctuation4046
 
5.0%
Dash Punctuation4043
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
912135
16.7%
812132
16.7%
012129
16.7%
38092
11.1%
68089
11.1%
48089
11.1%
24055
 
5.6%
14049
 
5.6%
54046
 
5.6%
76
 
< 0.1%
ValueCountFrequency (%)
-4043
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80911
100.0%

Most frequent character per script

ValueCountFrequency (%)
912135
15.0%
812132
15.0%
012129
15.0%
38092
10.0%
68089
10.0%
48089
10.0%
24055
 
5.0%
14049
 
5.0%
.4046
 
5.0%
54046
 
5.0%
Other values (2)4049
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII80911
100.0%

Most frequent character per block

ValueCountFrequency (%)
912135
15.0%
812132
15.0%
012129
15.0%
38092
10.0%
68089
10.0%
48089
10.0%
24055
 
5.0%
14049
 
5.0%
.4046
 
5.0%
54046
 
5.0%
Other values (2)4049
 
5.0%

region_region_N
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
-0.09672187120406334
4002 
10.338923219239676
 
44

Length

Max length20
Median length20
Mean length19.97825012
Min length18

Characters and Unicode

Total characters80832
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.09672187120406334
2nd row-0.09672187120406334
3rd row-0.09672187120406334
4th row-0.09672187120406334
5th row-0.09672187120406334
ValueCountFrequency (%)
-0.096721871204063344002
98.9%
10.33892321923967644
 
1.1%
2021-04-24T16:05:07.061785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:07.180377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.096721871204063344002
98.9%
10.33892321923967644
 
1.1%

Most occurring characters

ValueCountFrequency (%)
016052
19.9%
38180
10.1%
28136
10.1%
68092
10.0%
18092
10.0%
78048
10.0%
48004
9.9%
94134
 
5.1%
.4046
 
5.0%
84046
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72784
90.0%
Other Punctuation4046
 
5.0%
Dash Punctuation4002
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
016052
22.1%
38180
11.2%
28136
11.2%
68092
11.1%
18092
11.1%
78048
11.1%
48004
11.0%
94134
 
5.7%
84046
 
5.6%
ValueCountFrequency (%)
-4002
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80832
100.0%

Most frequent character per script

ValueCountFrequency (%)
016052
19.9%
38180
10.1%
28136
10.1%
68092
10.0%
18092
10.0%
78048
10.0%
48004
9.9%
94134
 
5.1%
.4046
 
5.0%
84046
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII80832
100.0%

Most frequent character per block

ValueCountFrequency (%)
016052
19.9%
38180
10.1%
28136
10.1%
68092
10.0%
18092
10.0%
78048
10.0%
48004
9.9%
94134
 
5.1%
.4046
 
5.0%
84046
 
5.0%

region_region_O
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
-0.19279511513892258
3908 
5.186853407978876
 
138

Length

Max length20
Median length20
Mean length19.89767672
Min length17

Characters and Unicode

Total characters80506
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.19279511513892258
2nd row-0.19279511513892258
3rd row-0.19279511513892258
4th row-0.19279511513892258
5th row-0.19279511513892258
ValueCountFrequency (%)
-0.192795115138922583908
96.6%
5.186853407978876138
 
3.4%
2021-04-24T16:05:07.482390image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:07.648587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.192795115138922583908
96.6%
5.186853407978876138
 
3.4%

Most occurring characters

ValueCountFrequency (%)
115770
19.6%
512000
14.9%
911862
14.7%
211724
14.6%
88368
10.4%
74322
 
5.4%
04046
 
5.0%
.4046
 
5.0%
34046
 
5.0%
-3908
 
4.9%
Other values (2)414
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72552
90.1%
Other Punctuation4046
 
5.0%
Dash Punctuation3908
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
115770
21.7%
512000
16.5%
911862
16.3%
211724
16.2%
88368
11.5%
74322
 
6.0%
04046
 
5.6%
34046
 
5.6%
6276
 
0.4%
4138
 
0.2%
ValueCountFrequency (%)
-3908
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80506
100.0%

Most frequent character per script

ValueCountFrequency (%)
115770
19.6%
512000
14.9%
911862
14.7%
211724
14.6%
88368
10.4%
74322
 
5.4%
04046
 
5.0%
.4046
 
5.0%
34046
 
5.0%
-3908
 
4.9%
Other values (2)414
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII80506
100.0%

Most frequent character per block

ValueCountFrequency (%)
115770
19.6%
512000
14.9%
911862
14.7%
211724
14.6%
88368
10.4%
74322
 
5.4%
04046
 
5.0%
.4046
 
5.0%
34046
 
5.0%
-3908
 
4.9%
Other values (2)414
 
0.5%

region_region_P
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size331.6 KiB
-0.2992292959191772
3697 
3.3419187681078633
 
349

Length

Max length19
Median length19
Mean length18.91374197
Min length18

Characters and Unicode

Total characters76525
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.2992292959191772
2nd row-0.2992292959191772
3rd row-0.2992292959191772
4th row-0.2992292959191772
5th row-0.2992292959191772
ValueCountFrequency (%)
-0.29922929591917723697
91.4%
3.3419187681078633349
 
8.6%
2021-04-24T16:05:07.936115image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:08.034782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.29922929591917723697
91.4%
3.3419187681078633349
 
8.6%

Most occurring characters

ValueCountFrequency (%)
922531
29.4%
218485
24.2%
18441
 
11.0%
78092
 
10.6%
04046
 
5.3%
.4046
 
5.3%
-3697
 
4.8%
53697
 
4.8%
31396
 
1.8%
81047
 
1.4%
Other values (2)1047
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68782
89.9%
Other Punctuation4046
 
5.3%
Dash Punctuation3697
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
922531
32.8%
218485
26.9%
18441
 
12.3%
78092
 
11.8%
04046
 
5.9%
53697
 
5.4%
31396
 
2.0%
81047
 
1.5%
6698
 
1.0%
4349
 
0.5%
ValueCountFrequency (%)
-3697
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76525
100.0%

Most frequent character per script

ValueCountFrequency (%)
922531
29.4%
218485
24.2%
18441
 
11.0%
78092
 
10.6%
04046
 
5.3%
.4046
 
5.3%
-3697
 
4.8%
53697
 
4.8%
31396
 
1.8%
81047
 
1.4%
Other values (2)1047
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII76525
100.0%

Most frequent character per block

ValueCountFrequency (%)
922531
29.4%
218485
24.2%
18441
 
11.0%
78092
 
10.6%
04046
 
5.3%
.4046
 
5.3%
-3697
 
4.8%
53697
 
4.8%
31396
 
1.8%
81047
 
1.4%
Other values (2)1047
 
1.4%

region_region_Q
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
-0.09606890351720607
4010 
10.409195518931869
 
36

Length

Max length20
Median length20
Mean length19.98220465
Min length18

Characters and Unicode

Total characters80848
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.09606890351720607
2nd row-0.09606890351720607
3rd row-0.09606890351720607
4th row-0.09606890351720607
5th row-0.09606890351720607
ValueCountFrequency (%)
-0.096068903517206074010
99.1%
10.40919551893186936
 
0.9%
2021-04-24T16:05:08.321203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:08.431947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.096068903517206074010
99.1%
10.40919551893186936
 
0.9%

Most occurring characters

ValueCountFrequency (%)
024132
29.8%
612066
14.9%
98164
 
10.1%
78020
 
9.9%
14154
 
5.1%
84082
 
5.0%
54082
 
5.0%
.4046
 
5.0%
34046
 
5.0%
-4010
 
5.0%
Other values (2)4046
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72792
90.0%
Other Punctuation4046
 
5.0%
Dash Punctuation4010
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
024132
33.2%
612066
16.6%
98164
 
11.2%
78020
 
11.0%
14154
 
5.7%
84082
 
5.6%
54082
 
5.6%
34046
 
5.6%
24010
 
5.5%
436
 
< 0.1%
ValueCountFrequency (%)
-4010
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80848
100.0%

Most frequent character per script

ValueCountFrequency (%)
024132
29.8%
612066
14.9%
98164
 
10.1%
78020
 
9.9%
14154
 
5.1%
84082
 
5.0%
54082
 
5.0%
.4046
 
5.0%
34046
 
5.0%
-4010
 
5.0%
Other values (2)4046
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII80848
100.0%

Most frequent character per block

ValueCountFrequency (%)
024132
29.8%
612066
14.9%
98164
 
10.1%
78020
 
9.9%
14154
 
5.1%
84082
 
5.0%
54082
 
5.0%
.4046
 
5.0%
34046
 
5.0%
-4010
 
5.0%
Other values (2)4046
 
5.0%

region_region_R
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.7 KiB
-0.10303257374565544
3996 
9.705668446840741
 
50

Length

Max length20
Median length20
Mean length19.96292635
Min length17

Characters and Unicode

Total characters80770
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.10303257374565544
2nd row-0.10303257374565544
3rd row-0.10303257374565544
4th row-0.10303257374565544
5th row-0.10303257374565544
ValueCountFrequency (%)
-0.103032573745655443996
98.8%
9.70566844684074150
 
1.2%
2021-04-24T16:05:08.698259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:08.803035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.103032573745655443996
98.8%
9.70566844684074150
 
1.2%

Most occurring characters

ValueCountFrequency (%)
516034
19.9%
412188
15.1%
012088
15.0%
311988
14.8%
78092
10.0%
64146
 
5.1%
.4046
 
5.0%
14046
 
5.0%
-3996
 
4.9%
23996
 
4.9%
Other values (2)150
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72728
90.0%
Other Punctuation4046
 
5.0%
Dash Punctuation3996
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
516034
22.0%
412188
16.8%
012088
16.6%
311988
16.5%
78092
11.1%
64146
 
5.7%
14046
 
5.6%
23996
 
5.5%
8100
 
0.1%
950
 
0.1%
ValueCountFrequency (%)
-3996
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80770
100.0%

Most frequent character per script

ValueCountFrequency (%)
516034
19.9%
412188
15.1%
012088
15.0%
311988
14.8%
78092
10.0%
64146
 
5.1%
.4046
 
5.0%
14046
 
5.0%
-3996
 
4.9%
23996
 
4.9%
Other values (2)150
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII80770
100.0%

Most frequent character per block

ValueCountFrequency (%)
516034
19.9%
412188
15.1%
012088
15.0%
311988
14.8%
78092
10.0%
64146
 
5.1%
.4046
 
5.0%
14046
 
5.0%
-3996
 
4.9%
23996
 
4.9%
Other values (2)150
 
0.2%

region_region_S
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.7 KiB
-0.12677667555426214
3988 
7.887886282141748
 
58

Length

Max length20
Median length20
Mean length19.95699456
Min length17

Characters and Unicode

Total characters80746
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.12677667555426214
2nd row-0.12677667555426214
3rd row-0.12677667555426214
4th row-0.12677667555426214
5th row-0.12677667555426214
ValueCountFrequency (%)
-0.126776675554262143988
98.6%
7.88788628214174858
 
1.4%
2021-04-24T16:05:09.068640image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:09.170382image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.126776675554262143988
98.6%
7.88788628214174858
 
1.4%

Most occurring characters

ValueCountFrequency (%)
616010
19.8%
712138
15.0%
212080
15.0%
511964
14.8%
18092
10.0%
48092
10.0%
.4046
 
5.0%
-3988
 
4.9%
03988
 
4.9%
8348
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72712
90.1%
Other Punctuation4046
 
5.0%
Dash Punctuation3988
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
616010
22.0%
712138
16.7%
212080
16.6%
511964
16.5%
18092
11.1%
48092
11.1%
03988
 
5.5%
8348
 
0.5%
ValueCountFrequency (%)
-3988
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80746
100.0%

Most frequent character per script

ValueCountFrequency (%)
616010
19.8%
712138
15.0%
212080
15.0%
511964
14.8%
18092
10.0%
48092
10.0%
.4046
 
5.0%
-3988
 
4.9%
03988
 
4.9%
8348
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII80746
100.0%

Most frequent character per block

ValueCountFrequency (%)
616010
19.8%
712138
15.0%
212080
15.0%
511964
14.8%
18092
10.0%
48092
10.0%
.4046
 
5.0%
-3988
 
4.9%
03988
 
4.9%
8348
 
0.4%

region_region_T
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.4 KiB
-0.21102930544182744
3876 
4.738678345674891
 
170

Length

Max length20
Median length20
Mean length19.87394958
Min length17

Characters and Unicode

Total characters80410
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.21102930544182744
2nd row-0.21102930544182744
3rd row-0.21102930544182744
4th row-0.21102930544182744
5th row-0.21102930544182744
ValueCountFrequency (%)
-0.211029305441827443876
95.8%
4.738678345674891170
 
4.2%
2021-04-24T16:05:09.446397image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:09.549305image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.211029305441827443876
95.8%
4.738678345674891170
 
4.2%

Most occurring characters

ValueCountFrequency (%)
416014
19.9%
111798
14.7%
011628
14.5%
211628
14.5%
84386
 
5.5%
74386
 
5.5%
34216
 
5.2%
.4046
 
5.0%
94046
 
5.0%
54046
 
5.0%
Other values (2)4216
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72488
90.1%
Other Punctuation4046
 
5.0%
Dash Punctuation3876
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
416014
22.1%
111798
16.3%
011628
16.0%
211628
16.0%
84386
 
6.1%
74386
 
6.1%
34216
 
5.8%
94046
 
5.6%
54046
 
5.6%
6340
 
0.5%
ValueCountFrequency (%)
-3876
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80410
100.0%

Most frequent character per script

ValueCountFrequency (%)
416014
19.9%
111798
14.7%
011628
14.5%
211628
14.5%
84386
 
5.5%
74386
 
5.5%
34216
 
5.2%
.4046
 
5.0%
94046
 
5.0%
54046
 
5.0%
Other values (2)4216
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII80410
100.0%

Most frequent character per block

ValueCountFrequency (%)
416014
19.9%
111798
14.7%
011628
14.5%
211628
14.5%
84386
 
5.5%
74386
 
5.5%
34216
 
5.2%
.4046
 
5.0%
94046
 
5.0%
54046
 
5.0%
Other values (2)4216
 
5.2%

region_unknown
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size331.5 KiB
-0.3067652548435562
3676 
3.2598215873892844
370 

Length

Max length19
Median length19
Mean length18.90855166
Min length18

Characters and Unicode

Total characters76504
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.3067652548435562
2nd row-0.3067652548435562
3rd row-0.3067652548435562
4th row-0.3067652548435562
5th row3.2598215873892844
ValueCountFrequency (%)
-0.30676525484355623676
90.9%
3.2598215873892844370
 
9.1%
2021-04-24T16:05:09.801044image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-24T16:05:09.899393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.30676525484355623676
90.9%
3.2598215873892844370
 
9.1%

Most occurring characters

ValueCountFrequency (%)
515444
20.2%
611028
14.4%
28462
11.1%
38092
10.6%
48092
10.6%
07352
9.6%
85156
 
6.7%
.4046
 
5.3%
74046
 
5.3%
-3676
 
4.8%
Other values (2)1110
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68782
89.9%
Other Punctuation4046
 
5.3%
Dash Punctuation3676
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
515444
22.5%
611028
16.0%
28462
12.3%
38092
11.8%
48092
11.8%
07352
10.7%
85156
 
7.5%
74046
 
5.9%
9740
 
1.1%
1370
 
0.5%
ValueCountFrequency (%)
-3676
100.0%
ValueCountFrequency (%)
.4046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common76504
100.0%

Most frequent character per script

ValueCountFrequency (%)
515444
20.2%
611028
14.4%
28462
11.1%
38092
10.6%
48092
10.6%
07352
9.6%
85156
 
6.7%
.4046
 
5.3%
74046
 
5.3%
-3676
 
4.8%
Other values (2)1110
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII76504
100.0%

Most frequent character per block

ValueCountFrequency (%)
515444
20.2%
611028
14.4%
28462
11.1%
38092
10.6%
48092
10.6%
07352
9.6%
85156
 
6.7%
.4046
 
5.3%
74046
 
5.3%
-3676
 
4.8%
Other values (2)1110
 
1.5%

tempo_min
Real number (ℝ)

HIGH CORRELATION

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004750464388
Minimum-3.819901198
Maximum3.437658321
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:05:09.992692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-3.819901198
5-th percentile-1.14606348
Q1-0.4515601772
median-0.4515601772
Q30.3818437866
95-th percentile1.493049072
Maximum3.437658321
Range7.257559518
Interquartile range (IQR)0.8334039638

Descriptive statistics

Standard deviation0.990549342
Coefficient of variation (CV)208.5163178
Kurtosis0.617372449
Mean0.004750464388
Median Absolute Deviation (MAD)0.8334039638
Skewness0.5497477388
Sum19.22037892
Variance0.9811879989
MonotocityNot monotonic
2021-04-24T16:05:10.128675image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.38184378661415
35.0%
-0.45156017721157
28.6%
-1.14606348751
18.6%
1.493049072418
 
10.3%
2.326453036127
 
3.1%
-1.59749062794
 
2.3%
2.88205567852
 
1.3%
-1.84056678413
 
0.3%
-3.8199011989
 
0.2%
-2.0489177755
 
0.1%
Other values (2)5
 
0.1%
ValueCountFrequency (%)
-3.8199011989
 
0.2%
-2.3961694261
 
< 0.1%
-2.0489177755
 
0.1%
-1.84056678413
 
0.3%
-1.59749062794
 
2.3%
-1.14606348751
18.6%
-0.45156017721157
28.6%
0.38184378661415
35.0%
1.493049072418
 
10.3%
2.326453036127
 
3.1%
ValueCountFrequency (%)
3.4376583214
 
0.1%
2.88205567852
 
1.3%
2.326453036127
 
3.1%
1.493049072418
 
10.3%
0.38184378661415
35.0%
-0.45156017721157
28.6%
-1.14606348751
18.6%
-1.59749062794
 
2.3%
-1.84056678413
 
0.3%
-2.0489177755
 
0.1%

tempo_max
Real number (ℝ)

HIGH CORRELATION

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0074284799
Minimum-3.062504212
Maximum2.782999128
Zeros0
Zeros (%)0.0%
Memory size63.2 KiB
2021-04-24T16:05:10.268236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-3.062504212
5-th percentile-1.243903173
Q1-0.4645027277
median-0.4645027277
Q30.5746978661
95-th percentile1.354098311
Maximum2.782999128
Range5.84550334
Interquartile range (IQR)1.039200594

Descriptive statistics

Standard deviation0.9882921678
Coefficient of variation (CV)133.0409695
Kurtosis-0.5220682622
Mean0.0074284799
Median Absolute Deviation (MAD)1.039200594
Skewness0.03350073456
Sum30.05562967
Variance0.9767214089
MonotocityNot monotonic
2021-04-24T16:05:10.405439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.57469786611415
35.0%
-0.46450272771157
28.6%
-1.243903173751
18.6%
1.354098311418
 
10.3%
1.873698608127
 
3.1%
-1.89340354494
 
2.3%
2.39329890552
 
1.3%
-2.31557878513
 
0.3%
-3.0625042129
 
0.2%
-2.5429039155
 
0.1%
Other values (2)5
 
0.1%
ValueCountFrequency (%)
-3.0625042129
 
0.2%
-2.7377540271
 
< 0.1%
-2.5429039155
 
0.1%
-2.31557878513
 
0.3%
-1.89340354494
 
2.3%
-1.243903173751
18.6%
-0.46450272771157
28.6%
0.57469786611415
35.0%
1.354098311418
 
10.3%
1.873698608127
 
3.1%
ValueCountFrequency (%)
2.7829991284
 
0.1%
2.39329890552
 
1.3%
1.873698608127
 
3.1%
1.354098311418
 
10.3%
0.57469786611415
35.0%
-0.46450272771157
28.6%
-1.243903173751
18.6%
-1.89340354494
 
2.3%
-2.31557878513
 
0.3%
-2.5429039155
 
0.1%

y
Real number (ℝ≥0)

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.281759763
Minimum0
Maximum10
Zeros32
Zeros (%)0.8%
Memory size63.2 KiB
2021-04-24T16:05:10.546692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median8
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.88754171
Coefficient of variation (CV)0.3965444898
Kurtosis-0.2880394215
Mean7.281759763
Median Absolute Deviation (MAD)2
Skewness-0.9789980117
Sum29462
Variance8.337897125
MonotocityNot monotonic
2021-04-24T16:05:10.689379image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
101337
33.0%
81305
32.3%
3362
 
8.9%
7334
 
8.3%
1205
 
5.1%
2191
 
4.7%
5126
 
3.1%
959
 
1.5%
650
 
1.2%
445
 
1.1%
ValueCountFrequency (%)
032
 
0.8%
1205
 
5.1%
2191
 
4.7%
3362
 
8.9%
445
 
1.1%
5126
 
3.1%
650
 
1.2%
7334
 
8.3%
81305
32.3%
959
 
1.5%
ValueCountFrequency (%)
101337
33.0%
959
 
1.5%
81305
32.3%
7334
 
8.3%
650
 
1.2%
5126
 
3.1%
445
 
1.1%
3362
 
8.9%
2191
 
4.7%
1205
 
5.1%

Interactions

2021-04-24T16:04:29.176291image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:29.347153image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:29.493100image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:29.639069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:29.806124image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:29.952587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:30.101883image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:30.259139image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:30.407428image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:30.558773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:30.714491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:30.866965image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:31.012857image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:31.158458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:31.305057image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:31.450891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:31.597174image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:31.737363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:31.879140image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:32.027368image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:32.170887image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:32.334905image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:32.515842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:32.684225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:32.849045image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:32.992948image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:33.131482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:33.269922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:33.415366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:33.553732image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:33.692141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:33.835642image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:33.972324image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:34.113796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:34.257849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:34.408762image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:34.545471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:34.684731image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:34.829479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:34.965632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:35.110053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:35.248512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:35.390274image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:35.536882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:35.678780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:35.824577image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:35.972689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:36.117939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:36.257666image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:36.405663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:36.553524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:36.708046image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:36.880913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:37.028146image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:37.180850image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:37.338612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:37.502234image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:37.655995image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:38.353846image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:38.518114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:38.666525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:38.804286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:38.944269image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:39.082164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:39.241662image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:39.411184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:39.550792image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:39.695791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:39.836737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:39.983265image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:40.129422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:40.274988image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:40.413446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:40.563321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:40.727484image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:40.876763image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:41.038130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:41.198680image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:41.349894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:41.507273image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:41.656556image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:41.806189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:41.957415image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:42.111067image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:42.268278image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:42.419240image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:42.573533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:42.722842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:42.873877image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:43.038120image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:43.188497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:43.342963image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:43.497373image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:43.654955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:43.816066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:43.974664image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:44.126892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:44.269765image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:44.422334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:44.563189image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:44.707215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:44.856599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:44.998032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:45.144054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:45.295026image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:45.444402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:45.595184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:45.746692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:45.889771image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:46.036801image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:46.184926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:46.335644image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:46.484113image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:46.637984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:46.786015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:46.937101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:47.094996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:47.252112image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:47.435598image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:47.614980image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:47.786636image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:47.952683image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:48.108591image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:48.259212image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:48.412701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:48.570179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:48.721488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:48.885166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:49.057984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:49.215320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:49.990170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:50.160943image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:50.316041image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:50.472443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:50.621131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:50.769360image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:50.933176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:51.089139image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:51.235733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:51.385364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:51.540906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:51.690490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:51.849963image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:52.007867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:52.157553image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:52.303294image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:52.445897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:52.583536image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:52.724447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:52.887170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:53.029498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:53.176441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:53.330340image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:53.474501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:53.626405image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-24T16:04:53.782747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-24T16:05:10.914098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-24T16:05:11.409526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-24T16:05:11.927371image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-24T16:05:12.430983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-24T16:05:12.897119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-24T16:04:54.215488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-24T16:04:55.684108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-24T16:04:56.075367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-24T16:04:56.311173image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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0-1.866000-0.499932-0.955307-1.385544-1.9628621.4300451.4325730.6145762.2321724.337850-0.047216-0.301186-0.146929-0.230726-0.448075-0.194512-0.116854.474168-0.491218-0.084967-0.24607-0.169117-0.019258-0.096722-0.192795-0.299229-0.096069-0.103033-0.126777-0.211029-0.3067650.3818440.57469810
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Last rows

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